You are not stupid, you have a learning problem. (1)
你不笨，只是学习上有点问题。
And classification is the basis of the common Machine Learning problem.
分类是许多机器学习问题解决的基础。
In the experiment aspects, the results shows that this algorithm can deal with the unsupervised learning problem successfully.
实验结果表明，该算法能成功地解决很多非监督分类问题。
You can help yourself get on top of things by learning problem-solving strategies that help you to work out solutions to your problems.
学习解决问题的技巧有助于找到问题的解决方案，这可以使你在处理问题时游刃有余。
Statistical Learning Theory is based on a solid theoretical foundation. It provides an unified framework for solving the small sample learning problem.
统计学习理论具有坚实的理论基础，为解决小样本学习问题提供了统一的框架。
This model adopts the improved Relevance Verifier algorithm, which not only improves the accuracy of matching, but also solves the self-learning problem.
该模型采用改进的关联决策算法，使得系统不但提高了准确率，而且还解决了自我学习问题。
But if we have that baseline and show that that child went from 132 to 105, that's a very significant decline and is clear indication of their learning problem.
但是，如果我们有基线表明孩子的智商从132降到105，这是一个非常显着下降，明确地显示他们的学习遇到了问题。
This partial order structure can be extended to any concept learning problem, and therefore, it plays an instructive role in all algorithms for concept learning.
这种偏序结构可以推广到任何概念学习的问题中，从而对整个概念学习的算法有着重要的指导意义。
This paper presents a learning problem from positive examples based on multiple valued minimization paradigm. A new heuristic algorithm for the problem is given.
本文基于多值逻辑函数极小化提出一种正例学习问题，并对这一正例学习问题给出一个启发式学习算法。
Moreover, SVMs can change a nonlinear learning problem in to a linear learning problem in order to reduce the algorithm complexity by using the kernel function idea.
又由于采用了核函数思想，使它把非线性问题转化为线性问题来解决，降低了算法的复杂度。
The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.
该算法将主动式学习、有偏分类和增量学习结合起来，对相关反馈过程中的小样本有偏学习问题进行建模。
Moreover, SVM can convert a nonlinear learning problem into a linear learning problem in order to reduce the algorithm complexity by using the kernel function concept.
又由于采用了核函数思想，使它将非线性问题转化为线性问题来解决，降低了算法的复杂度。
SVM has better generalization and guarantee the local optimal solution is exactly the global optimal solution. SVM can solve the learning problem of a smaller number of samples.
支持向量机具有小样本、较好的泛化能力、全局最优解等特点，在状态识别领域中表现出优良的特性。
What all this means is that the learning problem consists in not only learning a word to color mapping, but also in learning the peculiar color “maps” your language uses in the first place.
这意味着，学习颜色的问题，不只是简单的在学习一种颜色的名称，而是在建立你的语系所赋予的色彩体系。
The method can transfer the learning problem into a second planning to acquire the optimal solution according to the principle of structure risk minimum under limited samples situation.
该算法能针对在样本有限的情况下，采用结构风险最小化准则，把学习问题转化为一个二次规划问题来获得最优解。
There are usually few training samples in the tasks of content-based remote sensing image retrieval, which will lead to over-learning problem while using this small data set for training.
提出一种基于多分类器协同训练的遥感图像检索方法，该方法在不同特征集上分别建立分类器，利用不同分类器的协同性自动标记未知样本，从而有效解决了小样本问题。
What all this means is that the learning problem consists in not only learning a word to color mapping, but also in learning the peculiar color "maps" your language USES in the first place.
这意味着，学习颜色的问题，不只是简单的在学习一种颜色的名称，而是在建立你的语系所赋予的色彩体系。
Many things such as learning and problem solving can be done by computers, though not in the same way as people do.
许多事情，如学习和解决问题都可以通过计算机来完成，尽管方式与人不同。
We have been learning about the environment at school and the problem of plastic.
我们在学校里一直在学习关于环境和塑料问题的知识。
Her final goal is to link the worlds of art and science back together: She believes that bringing the old recipes to life can help develop a kind of learning that highlights experimentation, teamwork, and problem-solving.
她的最终目标是将艺术和科学的世界重新连接在一起：她相信将古老的方法恢复生气，会有助于形成一种重视实验、团队合作和解决问题的学习方式。
Interestingly, those that test well on working memory tasks also seem to do well at learning, reading comprehension and problem solving.
且有趣的是，那些在工作记忆任务中测试成绩较好的人，其在学习，阅读理解和解决问题上的能力也都较好。
Notice something important here: in the classification problem, the goal of the learning algorithm is to minimize the error with respect to the given inputs.
请注意这里提到的一个问题：在分类问题中，学习算法的目标是把给定输入中的错误最小化。
Learning to read is made easier when teachers create an environment where children are given the opportunity to solve the problem of learning to read by reading.
如果老师创造出一种环境，使孩子们能够通过阅读来解决阅读的问题的话，学习阅读就变的简单多了。
In a non-judgmental environment Powerful Questions can contribute to learning and problem solving in a way that a veiled "oh, shut up already" never would!
在不带个人评判的环境中，“强力问题”可以开启学习和问题解决之门，而暗藏的“噢，快闭嘴吧”之类的想法也会悄然无踪。
Another part of the problem of learning language is you have to figure out what the boundaries are between the words.
学习语言的另一个问题是，你必须得确定单词之间的界线是什么？
All this just further complicates the problem of learning and retaining the right lessons from the past.
所有这些都会使从过去学习教训或记住这些教训变得更加复杂。
More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine.
更一般地说，对于那些有用的分类系统，和容易判断的分类系统，分类学习都适用。
The problem is, of course, that the long break isn't conducive to learning.
当然问题是，如此长的假期对学习并无益处。
The problem with a completely new programming paradigm isn't learning a new language.
这是个全新的编程范式，并不像学习一门新语言那么简单。
The problem with a completely new programming paradigm isn't learning a new language.
这是个全新的编程范式，并不像学习一门新语言那么简单。
应用推荐